Adding geographical detail to social surveys:

Estimating local disability prevalence

Alan MarshallESDS Government

15th April 2010

The problem

• Researchers require detailed local information, for example, to provide appropriate services. BUT:

• The data that is available for small areas (districts, wards) often lacks detail– Census question on limiting long term illness

and disability

• Detailed data sources lack geography (confidentiality) – HSE has information on specific disabilities but

does not distinguish district of residence

• Combining locally available data with survey data offers a solution to this problem

Disability age pattern

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Observed rates Model rates

Disability rates (Higher severity) by age in England (females)

Source: Health Survey for England 2001

Many disability types are strongly linked to age

Prevalence ratio method

• Multiply the national disability rate (HSE) and the local population count at each age.

• This approach is used by the POPPI and PANSI websites to estimate mobility and personal care disability.

• Developed by the Institute for Public Care.

• Designed to help explore the possible impact that demography and certain conditions may have on populations.

• http://www.pansi.org.uk/• http://www.poppi.org.uk/

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Population

Bolton population pyramid – 2001 and 2021

Males Females

Grey bars indicate the population in 2001Clear bars indicate the population in 2021 Source: ONS MYE and pop projections

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Manchester population pyramid – 2001 and 2021

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Grey bars indicate the population in 2001Clear bars indicate the population in 2021 Source: ONS - MYE and pop projections

Including further local information

• Disability is linked to characteristics other than age that are available for local areas in the census

• E.g. LLTI increases the risk of having a personal care disability

If two districts with same age structure But one has higher level of LLTIThen we would expect higher levels of personal care disability

Relational models

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LLTI - Census Personal care - HSEPersonal care - Modelled

England - MalesLLTI and Personal care disability prevalence

Two adjustments relate the LLTI curve to the PC curve.

Comparing PANSI and relational estimates of personal care disability

Personal care prevalence (2008) (18-64)

SIR PANSI

Relational estimate

South Bucks 0.66 5.0 2.4 Brighton 0.98 4.1 4.3 Bury 1.05 4.7 5.2 Pendle 1.16 4.8 6.1 Wakefield 1.22 4.8 6.3 Easington 1.63 4.8 9.9

SIR = Standardised illness ratio

Other approaches – Area classifications

• People with similar socio-demographic characteristics cluster together within certain areas

• Classify areas into groups according to the socio-demographic characteristics of their populations

• ACORN MOSAIC – commercial classifications

• ONS classifications (since 1970s)

• HSE (2001) includes ONS area classification and urban/rural classifications

ONS Area classifications

ONS district classification (2001) SIRs – Census 2001SIR (2001)

Evaluating local estimates

• Catch 22 – if there were local estimates then we wouldn’t need to create them!

• Compare with proxy data - administrative records

• Seek opinions of local experts

• Compare with locally conducted surveys

• Compare estimates produced using different methods

Disability estimates from mythesis will soon be at:

http://www.ccsr.ac.uk/staff/am.htm

POPPI and PANSI websites:

http://www.pansi.org.uk/http://www.poppi.org.uk/

Output area classification group

http://areaclassification.org.uk/

Bajekal, M., Scholes, S.,Pickering, K. and Purdon, S.(2004). Synthetic estimation ofhealthy lifestyle indicators: Stage1 report. NatCen, London

Skinner, C. (1993). The Use ofSynthetic Estimation Techniquesto Produce Small AreaEstimates. New MethodologySeries NM18. OPCS. London.

Resources References